Currently, after fruit (for example apples) is harvested, the fruit is stored in a controlled atmosphere or refrigerated air environment for up to 12 months, until the fruit is ready for sorting into different quality grades and packing into boxes for the market. It is estimated that typically 10% to 40% of harvested apples are not suitable for the fresh fruit market—usually due to defects or issues with fruit quality. Removing defective or inferior apples during the harvest process would achieve significant cost savings in post-harvest storage and packing.
Further, storing defective fruit with otherwise uncontaminated fruits frequently results in the spread of disease and pests. Early identification and removal of defective fruit significantly reduces postharvest disease/pest problems. Machine vision systems are an effective tool for identifying defective fruit. Machine vision systems also create a storable and traceable record that permits growers and packinghouses to have detailed information (e.g., size, color, tree and orchard, etc.) about the fruit in each harvested lot/batch—which greatly enhances inventory management and product traceability. However, currently available machine vision systems are fragile, complex, and too bulky and expensive—and are not generally suitable for the field/harvest environment. There are currently no mobile harvest-point machine vision-based systems available to apple producers.
The need exists for a fruit harvesting system that quickly and efficiently separates defective fruit from fruit intended for the fresh produce market—and is functional in a harvest environment. The system described herein comprises a modular harvest-point machine vision system that singulates and records individual fruits, and then separates the fruit into various bins based on the grade and quality of the fruit. The system described herein is fast, relatively compact, and durable enough to function under harvest conditions.